SYSTEMS METHODS FOR ANALYSIS OF HETEROGENEOUS GLIOBLASTOMA DATASETS TOWARDS ELUCIDATION OF INTER-TUMOURAL RESISTANCE PATHWAYS AND NEW THERAPEUTIC TARGETS

In this PhD thesis is described an endeavour to compile litterature about Glioblastoma key molecular mechanisms into a directed network followin Disease Maps standards, analyse its topology and compare results with quantitative analysis of multi-omics datasets in order to investigate Glioblastoma resistance mechanisms. The work also integrated implementation of Data Management good practices and procedures

Genomic Exploration of Distinct Molecular Phenotypes Steering Temozolomide Resistance Development in Patient-Derived Glioblastoma Cells

Chemotherapy using temozolomide is the standard treatment for patients with glioblastoma. Despite treatment, prognosis is still poor largely due to the emergence of temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. To induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells’ molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell RNA sequencing on four of the cell cultures to track the evolution of temozolomide resistance. The induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as “adaptive” (ADA) or “non-adaptive” (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. The adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumor’s ability to survive. Depending on the tumor’s adaptability potential, subpopulations with acquired resistance mechanisms may arise.</p

Ex vivo drug sensitivity screening predicts response to temozolomide in glioblastoma patients and identifies candidate biomarkers

Background: Patient-derived glioma stem-like cells (GSCs) have become the gold-standard in neuro-oncological research; however, it remains to be established whether loss of in situ microenvironment affects the clinically-predictive value of this model. We implemented a GSC monolayer system to investigate in situ-in vitro molecular correspondence and the relationship between in vitro and patient response to temozolomide (TMZ). Methods: DNA/RNA-sequencing was performed on 56 glioblastoma tissues and 19 derived GSC cultures. Sensitivity to TMZ was screened across 66 GSC cultures. Viability readouts were related to clinical parameters of corresponding patients and whole-transcriptome data. Results: Tumour DNA and RNA sequences revealed strong similarity to corresponding GSCs despite loss of neuronal and immune interactions. In vitro TMZ screening yielded three response categories which significantly correlated with patient survival, therewith providing more specific prediction than the binary MGMT marker. Transcriptome analysis identified 121 genes related to TMZ sensitivity of which 21were validated in external datasets. Conclusion:GSCs retain patient-unique hallmark gene expressions despite loss of their natural environment. Drug screening using GSCs predicted patient response to TMZ more specifically than MGMT status, while transcriptome analysis identified potential biomarkers for this response. GSC drug screening therefore provides a tool to improve drug development and precision medicine for glioblastoma.</p

Modélisation de l’interaction entre gènes pour évaluer la durabilité de résistances variétales

2nd year engineering internship repor

Generation, characterization, and drug sensitivities of 12 patient-derived IDH1-mutant glioma cell cultures

Background: Mutations of the isocitrate dehydrogenase (IDH) gene occur in over 80% of low-grade gliomas and secondary glioblastomas. Despite considerable efforts, endogenous in vitro IDH-mutated glioma models remain scarce. Availability of these models is key for the development of new therapeutic interventions. Methods: Cell cultures were established from fresh tumor material and expanded in serum-free culture media. D-2-Hydroxyglutarate levels were determined by mass spectrometry. Genomic and transcriptomic profiling were carried out on the Illumina Novaseq platform, methylation profiling was performed with the Infinium MethylationEpic BeadChip array. Mitochondrial respiration was measured with the Seahorse XF24 Analyzer. Drug screens were performed with an NIH FDA-approved anti-cancer drug set and two IDH-mutant specific inhibitors. Results: A set of twelve patient-derived IDHmt cell cultures was established. We confirmed high concordance in driver mutations, copy numbers and methylation profiles between the tumors and derived cultures. Homozygous deletion of CDKN2A/B was observed in all cultures. IDH-mutant cultures had lower mitochondrial reserve capacity. IDH-mutant specific inhibitors did not affect cell viability or global gene expression. Screening of 107 FDA-approved anti-cancer drugs identified nine compounds with potent activity against IDHmt gliomas, including three compounds with favorable pharmacokinetic characteristics for CNS penetration: Teniposide, omacetaxine mepesuccinate, and marizomib. Conclusions: Our twelve IDH-mutant cell cultures show high similarity to the parental tissues and offer a unique tool to study the biology and drug sensitivities of high-grade IDHmt gliomas in vitro. Our drug screening studies reveal lack of sensitivity to IDHmt inhibitors, but sensitivity to a set of nine available anti-cancer agents

U-BIOPRED accessible handprint: Combining omics platforms to identify stable asthma subphenotypes

Background: The U-BIOPRED hypothesis is that performing clustering based on multiple omics platforms (handprints) is relevant to address the unmet need in severe asthma classification. Methods: We gathered the blood compartment-related (blood and urine) data in our adult asthma cohort (Shaw, ERJ 2015). We used Similarity Network Fusion and stability assessment to identify optimal omics platforms and cluster numbers combinations. We selected based on deviation from ideal stability (DIS). Results: A combination of transcriptomics, proteomics, lipidomics and metabolomics is giving the best results for K= 8 and 17 (DIS = 0.06 and 0.04). The comparison of patient’s allocation between the two shows separation in K=17 of clusters in K=8 (see figure). The comparison of clinical variables shows expected differences (immune cell numbers, BMI, FEV1) but other variables are different (medication, comorbidities, biomarkers) and some clusters identified don’t show any extreme values. Conclusion and perspectives: We have identified stable clusters of asthma patients within our cohort by multi-omics integration. The identification of molecular signatures and production of a predictive model is under way. The next steps are the validation with longitudinal measurements and external cohorts of severe asthma

Genomic exploration of distinct molecular phenotypes steering temozolomide resistance development in patient-derived glioblastoma cells

Chemotherapy using temozolomide is the standard treatment for patients with glioblastoma. Despite treatment, prognosis is still poor largely due to the emergence of temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. To induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells' molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell RNA sequencing on four of the cell cultures to track the evolution of temozolomide resistance. The induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as "adaptive" (ADA) or "non-adaptive" (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. The adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumor's ability to survive. Depending on the tumor's adaptability potential, subpopulations with acquired resistance mechanisms may arise. </p

Comparative analysis of deeply phenotyped GBM cohorts of 'short-term' and 'long-term' survivors.

peer reviewedBACKGROUND: Glioblastoma (GBM) is an aggressive brain cancer that typically results in death in the first 15 months after diagnosis. There have been limited advances in finding new treatments for GBM. In this study, we investigated molecular differences between patients with extremely short (≤ 9 months, Short term survivors, STS) and long survival (≥ 36 months, Long term survivors, LTS). METHODS: Patients were selected from an in-house cohort (GLIOTRAIN-cohort), using defined inclusion criteria (Karnofsky score > 70; age < 70 years old; Stupp protocol as first line treatment, IDH wild type), and a multi-omic analysis of LTS and STS GBM samples was performed. RESULTS: Transcriptomic analysis of tumour samples identified cilium gene signatures as enriched in LTS. Moreover, Immunohistochemical analysis confirmed the presence of cilia in the tumours of LTS. Notably, reverse phase protein array analysis (RPPA) demonstrated increased phosphorylated GAB1 (Y627), SRC (Y527), BCL2 (S70) and RAF (S338) protein expression in STS compared to LTS. Next, we identified 25 unique master regulators (MR) and 13 transcription factors (TFs) belonging to ontologies of integrin signalling and cell cycle to be upregulated in STS. CONCLUSION: Overall, comparison of STS and LTS GBM patients, identifies novel biomarkers and potential actionable therapeutic targets for the management of GBM